Understanding and optimising helmet-related and other health and social effects of the first North American university campus skatepark
Bibliographic record
Abstract
Skateboarding has growing societal uptake, as seen through its inclusion in 2021’s Summer Olympics. To promote positive health and social outcomes, skateparks are being developed around the world. A challenge in optimising skateparks’ benefits lies in reducing injury risk, particularly head trauma. This study occurred at the University of British Columbia Skatepark (July–September 2019) with the goal of identifying and theoretically contextualising facilitators and barriers to helmet use. Participants (total n = 54, 92.6% male) were interviewed (n = 54) and surveyed (n = 27). We performed thematic analysis on the transcripts, finding that barriers to helmet use included helmet discomfort, low perceived risk of injury, cultural norms, and style, and facilitators included a belief that helmets promote safety, higher-risk skating activities, older and younger ages, and role modelling. We propose a conceptual model showing multiple points of intervention to promote skatepark safety beyond helmet use alone, integrating theories of sociology, social psychology, and public health.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".